提交

backend/app/screener/engine.py

@@ -56,6 +56,10 @@ def _ma(df: pd.DataFrame, p: dict) -> dict[str, pd.Series]:
     return {"ma": ind.ma(df["close"], period=int(p["period"]))}
 
 
+def _vol_ratio(df: pd.DataFrame, p: dict) -> dict[str, pd.Series]:
+    return {"vol_ratio": ind.vol_ratio(df["volume"], period=int(p["period"]))}
+
+
 def _boll(df: pd.DataFrame, p: dict) -> dict[str, pd.Series]:
     out = ind.bollinger(df["close"], period=int(p["period"]), std=float(p["std"]))
     return {"boll_upper": out["upper"], "boll_mid": out["mid"], "boll_lower": out["lower"]}
@@ -75,6 +79,16 @@ def _pct_chg(df: pd.DataFrame, p: dict) -> dict[str, pd.Series]:
     return {"pct_chg": df["pct_chg"]}
 
 
+def _amplitude(df: pd.DataFrame, p: dict) -> dict[str, pd.Series]:
+    # 当日振幅 %（通达信口径：(最高-最低)/昨收×100），依赖派生列 amplitude
+    return {"amplitude": df["amplitude"]}
+
+
+def _turnover_rate(df: pd.DataFrame, p: dict) -> dict[str, pd.Series]:
+    # 逐日换手率 %（candles.turnover 直读；ETF/缺数据日为 NaN，条件按 False 处理）
+    return {"turnover_rate": df["turnover"]}
+
+
 @dataclass(frozen=True)
 class FamilyDef:
     label: str                     # 族中文标签（条件回显/表头）
@@ -89,20 +103,24 @@ FAMILIES: dict[str, FamilyDef] = {
     "macd": FamilyDef("MACD", ("fast", "slow", "signal"), {"fast": 12, "slow": 26, "signal": 9}, 60, _macd),
     "rsi": FamilyDef("RSI", ("period",), {"period": 14}, 25, _rsi),
     "ma": FamilyDef("MA", ("period",), {"period": 20}, 25, _ma),
+    "vol_ratio": FamilyDef("量比", ("period",), {"period": 5}, 10, _vol_ratio),
     "boll": FamilyDef("BOLL", ("period", "std"), {"period": 20, "std": 2}, 25, _boll),
     "zhixing": FamilyDef("知行", ("m1", "m2", "m3", "m4"),
                          {"m1": 14, "m2": 28, "m3": 57, "m4": 114}, 114, _zhixing),
     "close": FamilyDef("收盘价", (), {}, 1, _close),
     "pct_chg": FamilyDef("日涨跌幅", (), {}, 1, _pct_chg),
+    "amplitude": FamilyDef("当日振幅", (), {}, 2, _amplitude),
+    "turnover_rate": FamilyDef("换手率", (), {}, 1, _turnover_rate),
 }
 
 INDICATOR_FAMILY: dict[str, str] = {
     "kdj_k": "kdj", "kdj_d": "kdj", "kdj_j": "kdj",
     "macd_dif": "macd", "macd_dea": "macd", "macd_hist": "macd",
-    "rsi": "rsi", "ma": "ma",
+    "rsi": "rsi", "ma": "ma", "vol_ratio": "vol_ratio",
     "boll_upper": "boll", "boll_mid": "boll", "boll_lower": "boll",
     "zhixing_dkx": "zhixing", "zhixing_trend": "zhixing",
     "close": "close", "pct_chg": "pct_chg",
+    "amplitude": "amplitude", "turnover_rate": "turnover_rate",
 }
 
 _IND_SUFFIX = {"kdj_k": "K", "kdj_d": "D", "kdj_j": "J",
@@ -317,23 +335,27 @@ async def _load_bars(session: AsyncSession, ts_codes: list[str],
     """
     symbols = [plain_code(t) for t in ts_codes]
     stmt = select(
-        Candle.symbol, Candle.ts, Candle.open, Candle.high, Candle.low, Candle.close,
+        Candle.symbol, Candle.ts, Candle.open, Candle.high, Candle.low, Candle.close, Candle.volume,
+        Candle.turnover,
     ).where(Candle.timeframe == "1d", Candle.ts >= min_date, Candle.ts <= target_date)
     if len(symbols) <= 2000:
         stmt = stmt.where(Candle.symbol.in_(set(symbols)))
     rows = (await session.execute(stmt)).all()
-    df = pd.DataFrame(rows, columns=["symbol", "trade_date", "open", "high", "low", "close"])
+    df = pd.DataFrame(rows, columns=["symbol", "trade_date", "open", "high", "low", "close", "volume", "turnover"])
     if not df.empty and len(symbols) > 2000:
         df = df[df["symbol"].isin(set(symbols))]
     df = df.sort_values(["symbol", "trade_date"]).reset_index(drop=True)
     if df.empty:
         df["ts_code"] = pd.Series(dtype=object)
         df["pct_chg"] = pd.Series(dtype=object)
+        df["amplitude"] = pd.Series(dtype=object)
     else:
+        prev_close = df.groupby("symbol")["close"].shift(1)
         df["pct_chg"] = df.groupby("symbol")["close"].pct_change() * 100
+        df["amplitude"] = (df["high"] - df["low"]) / prev_close * 100
         ts_map = {plain_code(t): t for t in ts_codes}
         df["ts_code"] = df["symbol"].map(ts_map)
-    return df[["ts_code", "trade_date", "open", "high", "low", "close", "pct_chg"]]
+    return df[["ts_code", "trade_date", "open", "high", "low", "close", "volume", "turnover", "pct_chg", "amplitude"]]
 
 
 def _f(v) -> float | None: